Big Data vs Data Science—What’s the Difference?
Today, there is an increasing amount of information generated across the globe, leading to the concept of big data.According to Forbes, by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.
While managing this huge amount of data can pose a challenge for many organizations, it equally opens quite a number of opportunities for business growth. However, two common terms used frequently when discussing the benefits of data are big data and data science.
Despite sharing several important common elements with data science, it is still important to know how it differs to big data—as both terms are commonly misunderstood. Here are some of the key differences between big data vs data science.
Big data is a term that is used to describes the large volume of data—whether structured, unstructured or semi-structured. However, for data science (also referred to as data-driven science), it is an interdisciplinary field that combines several areas such as mathematics, intelligent data capture, statistics, data cleansing, mining and programming to extract knowledge or insights from data in various forms, either structured or unstructured.
The concept of big data involves diverse data types which are generated from multiple data sources while for data science, it is a specialized area that involves the use of scientific programming tools, models andtechniques to process big data.
Data science is responsible for supporting the decision-making process in organizations as it provides techniques to obtain insights and relevant information from large data sets.
3. Basis of formation
Big data is gotten from various sources which include online users/traffic, online platforms, audio/video streams, live feeds, electronic device, data generated internally within organizations, system logs and many more.
However, for data science, it utilizes a scientific method to extract relevant information from big data by developing models and capturing complex patterns in it. For organizations, there is really no threshold to the amount of valuable data that can be collected, but data science is needed to use all this data to extract meaningful information for organizational decisions.
4. Areas of Application
Big data can be applied across multiple sectors including financial services, sports, health, hospitality, telecommunications, security and law enforcement, telecommunications, retail and optimizing business processes.
Data science relies on big data to improve a host of activities such a fraud/risk detection, internet search, web development, digital advertising, image/speech recognition amongst many others.
The general approach to big data is to achieve better business sustainability. It can equally be used to gain better business agility and establish realistic metrics and ROI for the business. Big data is also useful in understanding new markets, gaining new customers, and enhancing competitiveness.
For data science, however, it involves the extensive use of statistics, mathematics and other analytical tools to obtain relevant information from big data. Data science uses both theoretical and experimental approaches—in addition to deductive and inductive reasoning—to reveal all hidden insightful information from unstructured data, thus realizing the potential of big data.
While big data relates more with analytical technology (such as Hadoop, Java, Hive, etc.), distributed computing, and analytics tools and software, data science focuses on the strategies for business decisions, statistics and data structures, data dissemination using mathematics and several other similar methods.